{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 01_relation_similarity_analysis\n",
    "#\n",
    "# created by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on March 12, 2023\n",
    "# updated by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on March 22, 2023\n",
    "#\n",
    "# 该脚本展示了如何分析训练的关系嵌入.\n",
    "#\n",
    "# 需要的包:\n",
    "#          numpy\n",
    "#          csv\n",
    "#          matplotlib\n",
    "#          sklearn\n",
    "#\n",
    "# 需要的文件:\n",
    "#          ../01-model/ckpts/TransE_l1_All_DRKG_0/All_DRKG_TransE_l1_relation.npy\n",
    "#          ../01-model/ckpts/TransE_l2_All_DRKG_0/All_DRKG_TransE_l2_relation.npy\n",
    "#          ../01-model/ckpts/ComplEx_All_DRKG_0/All_DRKG_ComplEx_relation.npy\n",
    "#          ../01-model/ckpts/RotatE_All_DRKG_0/All_DRKG_RotatE_relation.npy\n",
    "#          ../../data/drkg/relations.tsv\n",
    "#\n",
    "# 源教程链接: https://github.com/gnn4dr/DRKG/blob/master/embedding_analysis/Relation_similarity_analysis.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRKG Relation Embedding Similarity Analysis\n",
    " \n",
    "这个 notebook 展示了如何分析训练的关系嵌入.\n",
    "\n",
    "在这个例子中, 我们首先加载训练的嵌入向量, 然后将它们映射回原始的关系名, 最后应用两种方法分析它们:\n",
    "\n",
    "- 投射嵌入进入低维空间并可视化它们的分布.\n",
    "- 使用余弦距离分析关系间的相似程度."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "from matplotlib import colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p ./result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Relation ID Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of relations: 107\n"
     ]
    }
   ],
   "source": [
    "rel2id = {}\n",
    "id2rel = {}\n",
    "\n",
    "with open(\"../../data/drkg/relations.tsv\", newline = '', encoding = 'utf-8') as csvfile:\n",
    "    reader = csv.DictReader(csvfile, delimiter='\\t', fieldnames=['id','rel'])\n",
    "    for row_val in reader:\n",
    "        id = row_val['id']\n",
    "        relation = row_val['rel']\n",
    "\n",
    "        rel2id[relation] = int(id)\n",
    "        id2rel[int(id)] = relation\n",
    "\n",
    "print(\"Number of relations: {}\".format(len(rel2id)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treatment relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "treatment_list = [\n",
    "'DRUGBANK::treats::Compound:Disease',\n",
    "'GNBR::T::Compound:Disease',\n",
    "'Hetionet::CtD::Compound:Disease'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DRUGBANK::treats::Compound:Disease',\n",
       " 'GNBR::T::Compound:Disease',\n",
       " 'Hetionet::CtD::Compound:Disease']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Relation Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(107, 400) (107, 400) (107, 400) (107, 200)\n"
     ]
    }
   ],
   "source": [
    "TransE_l1_rel_emb = np.load('../01-model/ckpts/TransE_l1_All_DRKG_0/All_DRKG_TransE_l1_relation.npy')\n",
    "TransE_l2_rel_emb = np.load('../01-model/ckpts/TransE_l2_All_DRKG_0/All_DRKG_TransE_l2_relation.npy')\n",
    "ComplEx_rel_emb = np.load('../01-model/ckpts/ComplEx_All_DRKG_0/All_DRKG_ComplEx_relation.npy')\n",
    "RotatE_rel_emb = np.load('../01-model/ckpts/RotatE_All_DRKG_0/All_DRKG_RotatE_relation.npy')\n",
    "print(TransE_l1_rel_emb.shape, TransE_l2_rel_emb.shape, ComplEx_rel_emb.shape, RotatE_rel_emb.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Relation Embedding Clustering\n",
    "\n",
    "这里我们使用 t-SNE 将关系嵌入降维, 然后可视化它们的分布."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将关系按照源数据集分类\n",
    "dataset_id = {}\n",
    "for rel_name, i in rel2id.items():\n",
    "    rel_key = rel_name.split('::')[0]\n",
    "    if dataset_id.get(rel_key, None) is None:\n",
    "        dataset_id[rel_key] = []\n",
    "    dataset_id[rel_key].append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载之前保存好的 embedded.\n"
     ]
    }
   ],
   "source": [
    "# 降维并转置, 降维的结果每次都不同\n",
    "try:\n",
    "    X_TransE_l1_embedded = np.load('./result/relation/X_TransE_l1_embedded.npy')\n",
    "    X_TransE_l2_embedded = np.load('./result/relation/X_TransE_l2_embedded.npy')\n",
    "    X_ComplEx_embedded = np.load('./result/relation/X_ComplEx_embedded.npy')\n",
    "    X_RotatE_embedded = np.load('./result/relation/X_RotatE_embedded.npy')\n",
    "    print(\"加载之前保存好的 embedded.\")\n",
    "except Exception as e :\n",
    "    print(\"重新计算 embedded.\")\n",
    "    X_TransE_l1_embedded = TSNE(n_components=2).fit_transform(TransE_l1_rel_emb).T\n",
    "    X_TransE_l2_embedded = TSNE(n_components=2).fit_transform(TransE_l2_rel_emb).T\n",
    "    X_ComplEx_embedded = TSNE(n_components=2).fit_transform(ComplEx_rel_emb).T\n",
    "    X_RotatE_embedded = TSNE(n_components=2).fit_transform(RotatE_rel_emb).T\n",
    "    np.save('./result/relation/X_TransE_l1_embedded.npy', X_TransE_l1_embedded)\n",
    "    np.save('./result/relation/X_TransE_l2_embedded.npy', X_TransE_l2_embedded)\n",
    "    np.save('./result/relation/X_ComplEx_embedded.npy', X_ComplEx_embedded)\n",
    "    np.save('./result/relation/X_RotatE_embedded.npy', X_RotatE_embedded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制，不画 treatments\n",
    "plt.figure(figsize = (9, 9))\n",
    "\n",
    "# TransE_l1\n",
    "plt.subplot(221)\n",
    "plt.title('A', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_TransE_l1_embedded[0][val], X_TransE_l1_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "    \n",
    "# TransE_l2\n",
    "plt.subplot(222)\n",
    "plt.title('B', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_TransE_l2_embedded[0][val], X_TransE_l2_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# ComplEx\n",
    "plt.subplot(223)\n",
    "plt.title('C', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_ComplEx_embedded[0][val], X_ComplEx_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# RotatE\n",
    "plt.subplot(224)\n",
    "plt.title('D', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_RotatE_embedded[0][val], X_RotatE_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "lgd = plt.legend(loc = 'lower center', ncols = 6, bbox_to_anchor=(-0.08, -0.3))\n",
    "plt.savefig('./result/relation/relation_no_treats.svg', bbox_extra_artists=(lgd,), bbox_inches='tight', format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制\n",
    "plt.figure(figsize = (9, 9))\n",
    "\n",
    "# TransE_l1\n",
    "ax_a = plt.subplot(221)\n",
    "plt.title('A', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_TransE_l1_embedded[0][val], X_TransE_l1_embedded[1][val], '.', label=key)\n",
    "\n",
    "'''\n",
    "for i in range(len(treatment_list)):\n",
    "    plt.annotate('treatments',\n",
    "                 xy=(X_TransE_l1_embedded[0][rel2id[treatment_list[i]]], X_TransE_l1_embedded[1][rel2id[treatment_list[i]]]),\n",
    "                 xytext=(5.0, -1.0),\n",
    "                 arrowprops=dict(arrowstyle = '->', color = 'cyan', linewidth = 1.0),\n",
    "                )\n",
    "'''\n",
    "\n",
    "ax_a.add_patch(plt.Rectangle((0.825, 0.015), 0.15, 0.18, ls=\"--\", ec=\"green\", fc = \"none\", linewidth = \"2.0\", transform=ax_a.transAxes))\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "    \n",
    "# TransE_l2\n",
    "ax_b = plt.subplot(222)\n",
    "plt.title('B', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_TransE_l2_embedded[0][val], X_TransE_l2_embedded[1][val], '.', label=key)\n",
    "\n",
    "'''\n",
    "for i in range(len(treatment_list)):\n",
    "    plt.annotate('treatments',\n",
    "                 xy=(X_TransE_l2_embedded[0][rel2id[treatment_list[i]]], X_TransE_l2_embedded[1][rel2id[treatment_list[i]]]),\n",
    "                 xytext=(0.5, 3.0),\n",
    "                 arrowprops=dict(arrowstyle = '->', color = 'cyan', linewidth = 1.0),\n",
    "                )\n",
    "'''\n",
    "\n",
    "ax_b.add_patch(plt.Rectangle((0.02, 0.23), 0.18, 0.18, ls=\"--\", ec=\"green\", fc = \"none\", linewidth = \"2.0\", transform=ax_b.transAxes))\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# ComplEx\n",
    "ax_c = plt.subplot(223)\n",
    "plt.title('C', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_ComplEx_embedded[0][val], X_ComplEx_embedded[1][val], '.', label=key)\n",
    "\n",
    "'''\n",
    "for i in range(len(treatment_list)):\n",
    "    plt.annotate('treatments',\n",
    "                 xy=(X_ComplEx_embedded[0][rel2id[treatment_list[i]]], X_ComplEx_embedded[1][rel2id[treatment_list[i]]]),\n",
    "                 xytext=(-50.0, 35.0),\n",
    "                 arrowprops=dict(arrowstyle = '->', color = 'cyan', linewidth = 1.0),\n",
    "                )\n",
    "'''\n",
    "\n",
    "ax_c.add_patch(plt.Rectangle((0.65, 0.16), 0.33, 0.3, ls=\"--\", ec=\"green\", fc = \"none\", linewidth = \"2.0\", transform=ax_c.transAxes))\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# RotatE\n",
    "ax_d = plt.subplot(224)\n",
    "plt.title('D', loc = 'left', position = (-0.1, -0.1))\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_RotatE_embedded[0][val], X_RotatE_embedded[1][val], '.', label=key)\n",
    "\n",
    "'''\n",
    "for i in range(len(treatment_list)):\n",
    "    plt.annotate('treatments',\n",
    "                 xy=(X_RotatE_embedded[0][rel2id[treatment_list[i]]], X_RotatE_embedded[1][rel2id[treatment_list[i]]]),\n",
    "                 xytext=(-0.2, -1.5),\n",
    "                 arrowprops=dict(arrowstyle = '->', color = 'cyan', linewidth = 1.0),\n",
    "                )\n",
    "'''\n",
    "    \n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "lgd = plt.legend(loc = 'lower center', ncols = 6, bbox_to_anchor=(-0.08, -0.3))\n",
    "plt.savefig('./result/relation/relation.svg', bbox_extra_artists=(lgd,), bbox_inches='tight', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pair-wise Relation Embedding Cosine Similarity\n",
    "\n",
    "我们使用余弦距离计算成对的嵌入相似度, 然后输出最相似的 10 对."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算余弦相似度\n",
    "similarity_TransE_l1 = sklearn.metrics.pairwise.cosine_similarity(TransE_l1_rel_emb)\n",
    "similarity_TransE_l2 = sklearn.metrics.pairwise.cosine_similarity(TransE_l2_rel_emb)\n",
    "similarity_ComplEx = sklearn.metrics.pairwise.cosine_similarity(ComplEx_rel_emb)\n",
    "similarity_RotatE = sklearn.metrics.pairwise.cosine_similarity(RotatE_rel_emb)\n",
    "similaritys = [similarity_TransE_l1, similarity_TransE_l2, similarity_ComplEx, similarity_RotatE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_score(pair):\n",
    "    return pair[2]\n",
    "\n",
    "def output_similarity(similarity, file):\n",
    "    # 在每一行上, 利用相似度将元素 Index 按降序排列\n",
    "    idx = np.flip(np.argsort(similarity), axis = 1)\n",
    "    \n",
    "    max_pairs = []\n",
    "    for i in range(idx.shape[0]):\n",
    "        j = 1\n",
    "        while (similarity[i][idx[i][j]] > 0):\n",
    "            max_pairs.append((id2rel[idx[i][0]], id2rel[idx[i][j]], similarity[i][idx[i][j]]))\n",
    "            j += 1\n",
    "    \n",
    "    # 按照 score 降序 \n",
    "    max_pairs.sort(reverse=True, key = sort_score)\n",
    "    \n",
    "    sim_pairs = []\n",
    "    for i, pair in enumerate(max_pairs):\n",
    "        # 相似度矩阵是对称的, 因此有两个 score 完全一样的记录\n",
    "        if i % 2 == 0:\n",
    "            sim_pairs.append(pair)\n",
    "    \n",
    "    results_similarity = [\"{}\\t{}\\t{}\\n\".format(pair[0], pair[1], pair[2]) for pair in sim_pairs]\n",
    "    results_similarity = [\"relation_1\\trelation_2\\tpair-wise relation embedding cosine similarity\\n\"] + results_similarity\n",
    "    with open(file, 'w+') as f:\n",
    "        f.writelines(results_similarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [\n",
    "    \"./result/relation/cosine_similarity_TransE_l1_rel_emb.tsv\",\n",
    "    \"./result/relation/cosine_similarity_TransE_l2_rel_emb.tsv\",\n",
    "    \"./result/relation/cosine_similarity_ComplEx_rel_emb.tsv\",\n",
    "    \"./result/relation/cosine_similarity_RotatE_rel_emb.tsv\",\n",
    "]\n",
    "\n",
    "for i in range(len(similaritys)):\n",
    "    output_similarity(similaritys[i], files[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后, 我们绘制了成对相似度得分分布的直方图."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_similarity(similarity):\n",
    "    # 将相似度矩阵平铺\n",
    "    similarity = similarity.flatten()\n",
    "    \n",
    "    # 清除 self-compare 和 dup-compare\n",
    "    s = similarity < 0.99\n",
    "    s = np.unique(similarity[s])\n",
    "    print(f\"s.shape: {s.shape}.\")\n",
    "    print(f\"min: {s[0]:.3f}, max: {s[-1]:.3f}.\")\n",
    "    print(f\"> 0.90: {len(s[s>0.90])/s.shape[0]*100:.0f}%, >0.50: {len(s[s>0.50])/s.shape[0]*100:.0f}%\")\n",
    "    return s, s.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(dist, ax):\n",
    "    # N is the count in each bin, bins is the lower-limit of the bin\n",
    "    N, bins, patches = ax.hist(dist)\n",
    "    \n",
    "    '''\n",
    "    # We'll color code by height, but you could use any scalar\n",
    "    fracs = N / N.max()\n",
    "    \n",
    "    # we need to normalize the data to 0..1 for the full range of the colormap\n",
    "    norm = colors.Normalize(fracs.min(), fracs.max())\n",
    "    \n",
    "    # Now, we'll loop through our objects and set the color of each accordingly\n",
    "    for thisfrac, thispatch in zip(fracs, patches):\n",
    "        color = plt.cm.viridis(norm(thisfrac))\n",
    "        thispatch.set_facecolor(color)\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s.shape: (5671,).\n",
      "min: -0.873, max: 0.977.\n",
      "> 0.90: 1%, >0.50: 7%\n",
      "s.shape: (5669,).\n",
      "min: -0.774, max: 0.978.\n",
      "> 0.90: 0%, >0.50: 5%\n",
      "s.shape: (5670,).\n",
      "min: -0.208, max: 0.908.\n",
      "> 0.90: 0%, >0.50: 1%\n",
      "s.shape: (5671,).\n",
      "min: -0.241, max: 0.233.\n",
      "> 0.90: 0%, >0.50: 0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制，不画 treats\n",
    "plt.figure(figsize = (9, 9))\n",
    "\n",
    "# TransE_l1\n",
    "ax_a = plt.subplot(221)\n",
    "plt.title('A', loc = 'left', position = (-0.1, -0.1))\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[0])\n",
    "plot_hist(results[0], ax_a)\n",
    "ax_a.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "    \n",
    "# TransE_l2\n",
    "ax_b = plt.subplot(222)\n",
    "plt.title('B', loc = 'left', position = (-0.1, -0.1))\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[1])\n",
    "plot_hist(results[0], ax_b)\n",
    "ax_b.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "\n",
    "# ComplEx\n",
    "ax_c = plt.subplot(223)\n",
    "plt.title('C', loc = 'left', position = (-0.1, -0.1))\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[2])\n",
    "plot_hist(results[0], ax_c)\n",
    "ax_c.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "\n",
    "# RotatE\n",
    "ax_d = plt.subplot(224)\n",
    "plt.title('D', loc = 'left', position = (-0.1, -0.1))\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[3])\n",
    "plot_hist(results[0], ax_d)\n",
    "ax_d.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('./result/relation/relation-sim.svg', format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
